Henry Kennedy : Hierarchical Processing in the Cortex

Wednesday, 9 September, 2015 - 09:30 to 11:00
Using brain-wide retrograde tracing experiments in macaque, we are generating a consistent database of between area connections with projection densities, and distances (Markov et al., 2014a). The network is neither a sparse small-world graph nor scale-free (Markov et al., 2013). Local connectivity accounts for 80% of labeled neurons (Markov et al., 2011), meaning that cortex is heavily involved in local function. Importantly link weights, are highly characteristic across animals, follow a heavy-tailed lognormal distribution over 6 orders of magnitude, and decay exponentially with distance. 
The statistical properties of the cortex will give insight into the nature of the processing mode of the cortex. We have made a weighted network analysis that reveals a trade off between local and global efficiencies. An important finding is that a distance rule (EDR) predicts the binary features, the global and local communication efficiencies, clustered topography and the wire-minimization of the cortical graph (Ercsey-Ravasz et al., 2013, Song et al., 2014). We have therefore evaluated the shapes and dimensions of cortical areas, which place different parts of the same area in different neighborhoods, with respect to EDR predictions of connectivity. We have shown that in the visual cortex central representations are preferentially linked to the ventral stream and peripheral representations to the dorsal stream. 
Altogether, analysis of quantitative measures of connectivity suggest evolutionary optimization of areal shape, location and cortical folding and point to the need to consider the brain in space when considering the statistics of the inter-areal cortical network. 
The EDR rule tells us that that functionally related areas are spatially clustered, highly connected and share similar connectivity profiles. These findings underline the importance of weight-based hierarchical layering in cortical architecture and hierarchical processing (Markov et al., 2014b, Bastos et al., 2015). Tract-tracing also address the functional relations between areas by relating the inter-areal (global) and canonical (local) circuits in terms of predictive coding (Bastos et al., 2012) (Markov and Kennedy, 2013). 
Recently, it has been suggested that information transfer between cortical areas is dependent on thalamocortical connections via cortico-thalamo-cortical loops (Sherman, 2012). The topology of the cortico-thalamo-cortical loops suggests that these pathways are spatially highly limited. However, this is not the case of the claustrum, which can be thought of as the 7th layer of the insula and which has massively divergent inputs across the cortex (Markov et al., 2011). Understanding the complementary role of cortico-cortical pathways and clastrum is predictably a major question confronting neuroscience today.

Bastos AM, Usrey WM, Adams RA, Mangun GR, Fries P, Friston KJ (2012) Canonical microcircuits for predictive coding. Neuron 76:695-711.
Bastos AM, Vezoli J, Bosman CA, Schoffelen JM, Oostenveld R, Dowdall JR, De Weerd P, Kennedy H, Fries P (2015) Visual Areas Exert Feedforward and Feedback Influences through Distinct Frequency Channels. Neuron 85:390-401.
Ercsey-Ravasz M, Markov NT, Lamy C, Van Essen DC, Knoblauch K, Toroczkai Z, Kennedy H (2013) A predictive network model of cerebral cortical connectivity based on a distance rule. Neuron 80:184-197.
Markov NT, Ercsey-Ravasz M, Van Essen DC, Knoblauch K, Toroczkai Z, Kennedy H (2013) Cortical high-density counter-stream architectures. Science 342:1238406.
Markov NT, Ercsey-Ravasz MM, Ribeiro Gomes AR, Lamy C, Magrou L, Vezoli J, Misery P, Falchier A, Quilodran R, Gariel MA, Sallet J, Gamanut R, Huissoud C, Clavagnier S, Giroud P, Sappey-Marinier D, Barone P, Dehay C, Toroczkai Z, Knoblauch K, Van Essen DC, Kennedy H (2014a) A weighted and directed interareal connectivity matrix for macaque cerebral cortex. Cerebral Cortex 24:17-36.
Markov NT, Kennedy H (2013) The importance of being hierarchical. Curr Opin Neurobiol 23:187-194.
Markov NT, Misery P, Falchier A, Lamy C, Vezoli J, Quilodran R, Gariel MA, Giroud P, Ercsey-Ravasz M, Pilaz LJ, Huissoud C, Barone P, Dehay C, Toroczkai Z, Van Essen DC, Kennedy H, Knoblauch K (2011) Weight Consistency Specifies Regularities of Macaque Cortical Networks. Cerebral Cortex 21:1254-1272.
Markov NT, Vezoli J, Chameau P, Falchier A, Quilodran R, Huissoud C, Lamy C, Misery P, Giroud P, Barone P, Dehay C, Ullman S, Knoblauch K, Kennedy H (2014b) The Anatomy of Hierarchy: Feedforward and feedback pathways in macaque visual cortex. Journal of Comparative Neurology 522:225-259.
Sherman SM (2012) Thalamocortical interactions. Curr Opin Neurobiol 22:575-579.
Song HF, Kennedy H, Wang XJ (2014) Spatial embedding of structural similarity in the cerebral cortex. Proceedings of the National Academy of Sciences of the United States of America 111:16580-16585.